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DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for i...

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Autores principales: Jiang, Yi, Giase, Gina, Grennan, Kay, Shieh, Annie W., Xia, Yan, Han, Lide, Wang, Quan, Wei, Qiang, Chen, Rui, Liu, Sihan, White, Kevin P., Chen, Chao, Li, Bingshan, Liu, Chunyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179940/
https://www.ncbi.nlm.nih.gov/pubmed/32282793
http://dx.doi.org/10.1371/journal.pcbi.1007522
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author Jiang, Yi
Giase, Gina
Grennan, Kay
Shieh, Annie W.
Xia, Yan
Han, Lide
Wang, Quan
Wei, Qiang
Chen, Rui
Liu, Sihan
White, Kevin P.
Chen, Chao
Li, Bingshan
Liu, Chunyu
author_facet Jiang, Yi
Giase, Gina
Grennan, Kay
Shieh, Annie W.
Xia, Yan
Han, Lide
Wang, Quan
Wei, Qiang
Chen, Rui
Liu, Sihan
White, Kevin P.
Chen, Chao
Li, Bingshan
Liu, Chunyu
author_sort Jiang, Yi
collection PubMed
description Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.
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spelling pubmed-71799402020-05-05 DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data Jiang, Yi Giase, Gina Grennan, Kay Shieh, Annie W. Xia, Yan Han, Lide Wang, Quan Wei, Qiang Chen, Rui Liu, Sihan White, Kevin P. Chen, Chao Li, Bingshan Liu, Chunyu PLoS Comput Biol Research Article Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS. Public Library of Science 2020-04-13 /pmc/articles/PMC7179940/ /pubmed/32282793 http://dx.doi.org/10.1371/journal.pcbi.1007522 Text en © 2020 Jiang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jiang, Yi
Giase, Gina
Grennan, Kay
Shieh, Annie W.
Xia, Yan
Han, Lide
Wang, Quan
Wei, Qiang
Chen, Rui
Liu, Sihan
White, Kevin P.
Chen, Chao
Li, Bingshan
Liu, Chunyu
DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
title DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
title_full DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
title_fullStr DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
title_full_unstemmed DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
title_short DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
title_sort drams: a tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179940/
https://www.ncbi.nlm.nih.gov/pubmed/32282793
http://dx.doi.org/10.1371/journal.pcbi.1007522
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